Pandas Profiling Report

Dataset statistics

Number of variables	9
Number of observations	500
Missing cells	40
Missing cells (%)	0.9%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	35.3 KiB
Average record size in memory	72.3 B

Variable types

Numeric	7
Categorical	2

Alerts

`GRE Score` is highly overall correlated with `TOEFL Score` and 4 other fields	High correlation
`TOEFL Score` is highly overall correlated with `GRE Score` and 6 other fields	High correlation
`SOP` is highly overall correlated with `TOEFL Score` and 4 other fields	High correlation
`LOR` is highly overall correlated with `TOEFL Score` and 3 other fields	High correlation
`CGPA` is highly overall correlated with `GRE Score` and 5 other fields	High correlation
`Chance of Admit` is highly overall correlated with `GRE Score` and 6 other fields	High correlation
`Research` is highly overall correlated with `GRE Score` and 3 other fields	High correlation
`University Rating` is highly overall correlated with `GRE Score` and 5 other fields	High correlation
`GRE Score` has 15 (3.0%) missing values	Missing
`TOEFL Score` has 10 (2.0%) missing values	Missing
`University Rating` has 15 (3.0%) missing values	Missing
`Serial No.` is uniformly distributed	Uniform
`Serial No.` has unique values	Unique

Reproduction

Analysis started	2022-12-11 22:33:43.089306
Analysis finished	2022-12-11 22:33:53.566790
Duration	10.48 seconds
Software version	pandas-profiling vv3.5.0
Download configuration	config.json

Serial No.
Real number (ℝ)

UNIFORM
UNIQUE

Distinct	500
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	250.5

Minimum	1
Maximum	500
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	25.95
Q1	125.75
median	250.5
Q3	375.25
95-th percentile	475.05
Maximum	500
Range	499
Interquartile range (IQR)	249.5

Descriptive statistics

Standard deviation	144.48183
Coefficient of variation (CV)	0.57677378
Kurtosis	-1.2
Mean	250.5
Median Absolute Deviation (MAD)	125
Skewness	0
Sum	125250
Variance	20875
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1	1	0.2%
330	1	0.2%
343	1	0.2%
342	1	0.2%
341	1	0.2%
340	1	0.2%
339	1	0.2%
338	1	0.2%
337	1	0.2%
336	1	0.2%
Other values (490)	490	98.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
3	1	0.2%
4	1	0.2%
5	1	0.2%
6	1	0.2%
7	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%

Value	Count	Frequency (%)
500	1	0.2%
499	1	0.2%
498	1	0.2%
497	1	0.2%
496	1	0.2%
495	1	0.2%
494	1	0.2%
493	1	0.2%
492	1	0.2%
491	1	0.2%

GRE Score
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct	49
Distinct (%)	10.1%
Missing	15
Missing (%)	3.0%
Infinite	0
Infinite (%)	0.0%
Mean	316.55876

Minimum	290
Maximum	340
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	290
5-th percentile	298
Q1	308
median	317
Q3	325
95-th percentile	335
Maximum	340
Range	50
Interquartile range (IQR)	17

Descriptive statistics

Standard deviation	11.274704
Coefficient of variation (CV)	0.035616466
Kurtosis	-0.68446669
Mean	316.55876
Median Absolute Deviation (MAD)	8
Skewness	-0.051686583
Sum	153531
Variance	127.11896
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=49)

Value	Count	Frequency (%)
324	22	4.4%
312	22	4.4%
322	17	3.4%
327	17	3.4%
316	17	3.4%
321	17	3.4%
320	16	3.2%
314	16	3.2%
311	16	3.2%
325	15	3.0%
Other values (39)	310	62.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
290	2	0.4%
293	1	0.2%
294	2	0.4%
295	5	1.0%
296	5	1.0%
297	6	1.2%
298	10	2.0%
299	8	1.6%
300	12	2.4%
301	10	2.0%

Value	Count	Frequency (%)
340	9	1.8%
339	3	0.6%
338	4	0.8%
337	2	0.4%
336	5	1.0%
335	4	0.8%
334	7	1.4%
333	4	0.8%
332	7	1.4%
331	9	1.8%

TOEFL Score
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct	29
Distinct (%)	5.9%
Missing	10
Missing (%)	2.0%
Infinite	0
Infinite (%)	0.0%
Mean	107.18776

Minimum	92
Maximum	120
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	92
5-th percentile	98
Q1	103
median	107
Q3	112
95-th percentile	118
Maximum	120
Range	28
Interquartile range (IQR)	9

Descriptive statistics

Standard deviation	6.1128994
Coefficient of variation (CV)	0.057029829
Kurtosis	-0.66456537
Mean	107.18776
Median Absolute Deviation (MAD)	5
Skewness	0.10206773
Sum	52522
Variance	37.367539
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=29)

Value	Count	Frequency (%)
110	42	8.4%
105	37	7.4%
104	29	5.8%
107	28	5.6%
112	27	5.4%
106	26	5.2%
103	25	5.0%
102	24	4.8%
100	24	4.8%
99	22	4.4%
Other values (19)	206	41.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
92	1	0.2%
93	2	0.4%
94	2	0.4%
95	3	0.6%
96	6	1.2%
97	7	1.4%
98	10	2.0%
99	22	4.4%
100	24	4.8%
101	19	3.8%

Value	Count	Frequency (%)
120	9	1.8%
119	10	2.0%
118	10	2.0%
117	8	1.6%
116	16	3.2%
115	11	2.2%
114	18	3.6%
113	18	3.6%
112	27	5.4%
111	20	4.0%

University Rating
Categorical

HIGH CORRELATION
MISSING

Distinct	5
Distinct (%)	1.0%
Missing	15
Missing (%)	3.0%
Memory size	4.0 KiB

3.0	154
2.0	124
4.0	103
5.0	72
1.0	32

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	1455
Distinct characters	7
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	4.0
2nd row	4.0
3rd row	3.0
4th row	3.0
5th row	2.0

Common Values

Value	Count	Frequency (%)
3.0	154	30.8%
2.0	124	24.8%
4.0	103	20.6%
5.0	72	14.4%
1.0	32	6.4%
(Missing)	15	3.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
3.0	154	31.8%
2.0	124	25.6%
4.0	103	21.2%
5.0	72	14.8%
1.0	32	6.6%

Most occurring characters

Value	Count	Frequency (%)
.	485	33.3%
0	485	33.3%
3	154	10.6%
2	124	8.5%
4	103	7.1%
5	72	4.9%
1	32	2.2%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	970	66.7%
Other Punctuation	485	33.3%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	485	50.0%
3	154	15.9%
2	124	12.8%
4	103	10.6%
5	72	7.4%
1	32	3.3%

Other Punctuation

Value	Count	Frequency (%)
.	485	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	1455	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
.	485	33.3%
0	485	33.3%
3	154	10.6%
2	124	8.5%
4	103	7.1%
5	72	4.9%
1	32	2.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	1455	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
.	485	33.3%
0	485	33.3%
3	154	10.6%
2	124	8.5%
4	103	7.1%
5	72	4.9%
1	32	2.2%

SOP
Real number (ℝ)

Distinct	9
Distinct (%)	1.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.374

Minimum	1
Maximum	5
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	1.5
Q1	2.5
median	3.5
Q3	4
95-th percentile	5
Maximum	5
Range	4
Interquartile range (IQR)	1.5

Descriptive statistics

Standard deviation	0.99100362
Coefficient of variation (CV)	0.29371773
Kurtosis	-0.70571695
Mean	3.374
Median Absolute Deviation (MAD)	0.5
Skewness	-0.2289724
Sum	1687
Variance	0.98208818
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
4	89	17.8%
3.5	88	17.6%
3	80	16.0%
2.5	64	12.8%
4.5	63	12.6%
2	43	8.6%
5	42	8.4%
1.5	25	5.0%
1	6	1.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	6	1.2%
1.5	25	5.0%
2	43	8.6%
2.5	64	12.8%
3	80	16.0%
3.5	88	17.6%
4	89	17.8%
4.5	63	12.6%
5	42	8.4%

Value	Count	Frequency (%)
5	42	8.4%
4.5	63	12.6%
4	89	17.8%
3.5	88	17.6%
3	80	16.0%
2.5	64	12.8%
2	43	8.6%
1.5	25	5.0%
1	6	1.2%

LOR
Real number (ℝ)

Distinct	9
Distinct (%)	1.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.484

Minimum	1
Maximum	5
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	3
median	3.5
Q3	4
95-th percentile	5
Maximum	5
Range	4
Interquartile range (IQR)	1

Descriptive statistics

Standard deviation	0.92544957
Coefficient of variation (CV)	0.26562847
Kurtosis	-0.74574851
Mean	3.484
Median Absolute Deviation (MAD)	0.5
Skewness	-0.14529031
Sum	1742
Variance	0.85645691
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=9)

Value	Count	Frequency (%)
3	99	19.8%
4	94	18.8%
3.5	86	17.2%
4.5	63	12.6%
2.5	50	10.0%
5	50	10.0%
2	46	9.2%
1.5	11	2.2%
1	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
1.5	11	2.2%
2	46	9.2%
2.5	50	10.0%
3	99	19.8%
3.5	86	17.2%
4	94	18.8%
4.5	63	12.6%
5	50	10.0%

Value	Count	Frequency (%)
5	50	10.0%
4.5	63	12.6%
4	94	18.8%
3.5	86	17.2%
3	99	19.8%
2.5	50	10.0%
2	46	9.2%
1.5	11	2.2%
1	1	0.2%

CGPA
Real number (ℝ)

Distinct	184
Distinct (%)	36.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	8.57644

Minimum	6.8
Maximum	9.92
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	6.8
5-th percentile	7.638
Q1	8.1275
median	8.56
Q3	9.04
95-th percentile	9.6
Maximum	9.92
Range	3.12
Interquartile range (IQR)	0.9125

Descriptive statistics

Standard deviation	0.6048128
Coefficient of variation (CV)	0.070520263
Kurtosis	-0.5612784
Mean	8.57644
Median Absolute Deviation (MAD)	0.46
Skewness	-0.026612517
Sum	4288.22
Variance	0.36579852
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
8.76	9	1.8%
8	9	1.8%
8.56	7	1.4%
8.12	7	1.4%
8.45	7	1.4%
8.54	7	1.4%
8.66	6	1.2%
8.65	6	1.2%
8.64	6	1.2%
8.5	6	1.2%
Other values (174)	430	86.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
6.8	1	0.2%
7.2	1	0.2%
7.21	1	0.2%
7.23	1	0.2%
7.25	1	0.2%
7.28	1	0.2%
7.3	1	0.2%
7.34	2	0.4%
7.36	1	0.2%
7.4	1	0.2%

Value	Count	Frequency (%)
9.92	1	0.2%
9.91	1	0.2%
9.87	2	0.4%
9.86	1	0.2%
9.82	1	0.2%
9.8	3	0.6%
9.78	1	0.2%
9.76	2	0.4%
9.74	1	0.2%
9.7	2	0.4%

Research
Categorical

Distinct	2
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	4.0 KiB

1	280
0	220

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	500
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	0

Common Values

Value	Count	Frequency (%)
1	280	56.0%
0	220	44.0%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
1	280	56.0%
0	220	44.0%

Most occurring characters

Value	Count	Frequency (%)
1	280	56.0%
0	220	44.0%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	500	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
1	280	56.0%
0	220	44.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	500	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
1	280	56.0%
0	220	44.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	500	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
1	280	56.0%
0	220	44.0%

Chance of Admit
Real number (ℝ)

Distinct	61
Distinct (%)	12.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.72174

Minimum	0.34
Maximum	0.97
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	4.0 KiB

Quantile statistics

Minimum	0.34
5-th percentile	0.47
Q1	0.63
median	0.72
Q3	0.82
95-th percentile	0.94
Maximum	0.97
Range	0.63
Interquartile range (IQR)	0.19

Descriptive statistics

Standard deviation	0.1411404
Coefficient of variation (CV)	0.19555575
Kurtosis	-0.4546818
Mean	0.72174
Median Absolute Deviation (MAD)	0.1
Skewness	-0.28996621
Sum	360.87
Variance	0.019920614
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.71	23	4.6%
0.64	19	3.8%
0.73	18	3.6%
0.79	16	3.2%
0.72	16	3.2%
0.78	15	3.0%
0.76	14	2.8%
0.7	13	2.6%
0.68	13	2.6%
0.8	13	2.6%
Other values (51)	340	68.0%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.34	2	0.4%
0.36	2	0.4%
0.37	1	0.2%
0.38	2	0.4%
0.39	1	0.2%
0.42	4	0.8%
0.43	1	0.2%
0.44	3	0.6%
0.45	3	0.6%
0.46	5	1.0%

Value	Count	Frequency (%)
0.97	4	0.8%
0.96	8	1.6%
0.95	5	1.0%
0.94	13	2.6%
0.93	12	2.4%
0.92	9	1.8%
0.91	10	2.0%
0.9	9	1.8%
0.89	11	2.2%
0.88	4	0.8%

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:

Variable_type-Variable_type : Method, Range
Categorical-Categorical : Cramer's V, [0,1]
Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
Numerical-Numerical : Spearman's ρ, [-1,1]

The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

First rows
Last rows

	Serial No.	GRE Score	TOEFL Score	University Rating	SOP	LOR	CGPA	Research	Chance of Admit
0	1	337.0	118.0	4.0	4.5	4.5	9.65	1	0.92
1	2	324.0	107.0	4.0	4.0	4.5	8.87	1	0.76
2	3	NaN	104.0	3.0	3.0	3.5	8.00	1	0.72
3	4	322.0	110.0	3.0	3.5	2.5	8.67	1	0.80
4	5	314.0	103.0	2.0	2.0	3.0	8.21	0	0.65
5	6	330.0	115.0	5.0	4.5	3.0	9.34	1	0.90
6	7	321.0	109.0	NaN	3.0	4.0	8.20	1	0.75
7	8	308.0	101.0	2.0	3.0	4.0	7.90	0	0.68
8	9	302.0	102.0	1.0	2.0	1.5	8.00	0	0.50
9	10	323.0	108.0	3.0	3.5	3.0	8.60	0	0.45

	Serial No.	GRE Score	TOEFL Score	University Rating	SOP	LOR	CGPA	Research	Chance of Admit
490	491	307.0	105.0	2.0	2.5	4.5	8.12	1	0.67
491	492	297.0	99.0	4.0	3.0	3.5	7.81	0	0.54
492	493	298.0	101.0	4.0	2.5	4.5	7.69	1	0.53
493	494	300.0	95.0	2.0	3.0	1.5	8.22	1	0.62
494	495	301.0	99.0	3.0	2.5	2.0	8.45	1	0.68
495	496	332.0	108.0	5.0	4.5	4.0	9.02	1	0.87
496	497	337.0	117.0	5.0	5.0	5.0	9.87	1	0.96
497	498	330.0	120.0	5.0	4.5	5.0	9.56	1	0.93
498	499	312.0	103.0	4.0	4.0	5.0	8.43	0	0.73
499	500	327.0	113.0	4.0	4.5	4.5	9.04	0	0.84

Overview

Variables

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Other Punctuation

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Interactions

Correlations

Auto

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample